| --- |
| license: mit |
| base_model: |
| - microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
| --- |
| ## Clinical Decision Support Model 🩺📊 |
| Model Overview |
| This Clinical Decision Support Model is designed to assist healthcare providers in making data-driven decisions based on patient information. The model leverages advanced natural language processing (NLP) capabilities using the BiomedBERT architecture, fine-tuned specifically on a synthetic dataset of heart disease-related patient data. It provides personalized recommendations for patients based on their clinical profile. |
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| ## Model Use Case |
| The primary use case for this model is Clinical Decision Support in the domain of Cardiovascular Health. It helps healthcare professionals by: |
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| Evaluating patient health data. |
| Predicting clinical recommendations. |
| Reducing decision-making time and improving the quality of care. |
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| ## Inputs |
| The model expects input in the following format: |
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| Age: <int>, Gender: <Male/Female>, Weight: <int>, Smoking Status: <Never/Former/Current>, Diabetes: <0/1>, Hypertension: <0/1>, Cholesterol: <int>, Heart Disease History: <0/1>, Symptoms: <string>, Risk Score: <float> |
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| ## Output |
| The model predicts a recommendation from one of the following categories: |
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| Maintain healthy lifestyle |
| Immediate cardiologist consultation |
| Start statins, monitor regularly |
| Lifestyle changes, monitor |
| No immediate action |
| Increase statins, lifestyle changes |
| Start ACE inhibitors, monitor |
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| ## Example Input |
| Age: 70, Gender: Female, Weight: 66, Smoking Status: Never, Diabetes: 0, Hypertension: 1, Cholesterol: 258, Heart Disease History: 1, Symptoms: Chest pain, Risk Score: 6.1 |
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| ## Example Output |
| Recommendation: Start ACE inhibitors, monitor |
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| ## Model Training |
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| Base Model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
| Dataset: A synthetic dataset of 5000 patient examples with details like age, gender, symptoms, risk score, etc. |
| Fine-tuning Framework: Hugging Face Transformers. |
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| ## How to Use |
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| from transformers import pipeline |
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| #Load the model |
| model_path = "your_username/clinical_decision_support" |
| classifier = pipeline("text-classification", model=model_path) |
| |
| #Example input |
| input_text = "Age: 70, Gender: Female, Weight: 66, Smoking Status: Never, Diabetes: 0, Hypertension: 1, Cholesterol: 258, Heart Disease History: 1, Symptoms: Chest pain, Risk Score: 6.1" |
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| #Get prediction |
| prediction = classifier(input_text) |
| print(prediction) |
| |
| ## Limitations |
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| The model is based on synthetic data and may not fully generalize to real-world scenarios. |
| Recommendations are not a substitute for clinical expertise and should always be validated by a healthcare professional. |
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| ## Future Improvements |
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| Train on a larger, real-world dataset to enhance model performance. |
| Expand the scope to include recommendations for other medical domains. |
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| ## Acknowledgments |
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| Model fine-tuned using the Hugging Face Transformers library. |
| Base model provided by Microsoft: BiomedBERT. |
